An Automated Workflow for Real-Time Monitoring and Benchmarking of Connection Time in Drilling Operations
- Ali Karimi Vajargah (Occidental) | Reza Banirazi (Occidental) | Juan Pablo Mejia (Occidental) | Phillippe Haffner (Occidental)
- Document ID
- Society of Petroleum Engineers
- IADC/SPE International Drilling Conference and Exhibition, 3-5 March, Galveston, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2020. IADC/SPE International Drilling Conference and Exhibition
- 1.6 Drilling Operations, 6.3 Safety, 1.10 Drilling Equipment, 1.6.10 Running and Setting Casing, 1.10 Drilling Equipment, 1.12 Drilling Measurement, Data Acquisition and Automation, 1.12.6 Drilling Data Management and Standards, 2.2 Installation and Completion Operations
- invisible lost time, automated algorithm, connection time, Internet of Things, real-time data
- 3 in the last 30 days
- 126 since 2007
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|SPE Member Price:||USD 5.00|
|SPE Non-Member Price:||USD 28.00|
Invisible lost time (ILT) during drilling can add up to be extremely costly. Since a significant portion of ILT occurs during drilling and tripping operations (including running casing), the aim is to develop a workflow based on analytics algorithms that accurately identify and record how long it takes to make both drilling and tripping connections in real time using 1-second surface data. By capturing these KPIs, performance differences across the rig fleet were identified, and best practices were shared.
The developed workflow and algorithms for the drilling and tripping connection times can be applied to both active and historical wells in a fully automated fashion. The algorithm consumes 1-second surface drilling data to identify each connection. For more in-depth analysis, the drilling connection is divided into three stages: pre-connection, slip-to-slip, and post-connection. The algorithm relies on an advanced "rig state" (e.g., drilling, tripping in, tripping out, running casing) to determine the current state of the drilling process.
The algorithm was applied to more than 500 historical wells, drilled in three major business units in northwest Texas and southeast New Mexico. These wells had different levels of complexity, from shallow vertical wells to complex horizontal wells, and thus provided a very valuable data set for benchmarking and lookback studies. Through this process, more than 750,000 connections were identified. Analysis was done to determine a correlation between the connection performance and various factors such as rig type and contractor, crew, well design, hole depth, and drilling region. Best performers were identified for each unit to help determine the operational goals. The analysis showed that the drilling connection time varied significantly among different rigs, with a gap of more than 3 minutes for one of the business units, suggesting a huge improvement opportunity. In addition, the algorithm is currently being deployed at the rig site using the Internet of Things (IoT) Edge technology to run the application locally in real time and provide the drill site manager and driller with connection time, which had immediate impact on performance improvement.
The developed workflow in association with the algorithm, online visualization platform, and real-time monitoring, provides a powerful tool to improve efficiency, set realistic goals, make intelligent business decisions, and reduce the well delivery time without compromising safety. In addition, this study reveals that a reduction of up to 3% in drilling time of future wells can be achieved by lowering the current average connection time to the P10 value (best performers).
|File Size||1 MB||Number of Pages||17|
Al-Ghunaim, S. M., Sadiq, B. M., Siam, M., Nassar, I., & Aissa, R. K. (2017, March 6). Operations Efficiency: Improved Well Planning Methodology Based on Invisible Lost Time Smart KPIs. Society of Petroleum Engineers. doi:10.2118/183941-MS.
Andersen, K., Sjowall, P. A., Maidla, E. E., King, B., Thonhauser, G., & Zollner, P. (2009, January 1). Case History: Automated Performance Measurement of Crews and Drilling Equipment. Society of Petroleum Engineers. doi:10.2118/119746-MS.
Denney, D. (2011, September 1). Rigorous Drilling-Nonproductive-Time Determination and Eliminating Invisible Lost Time. Society of Petroleum Engineers. doi:10.2118/0911-0083-JPT.
El Afifi, S., Albassam, B., & Fahmy, F. A. (2015, September 15). Enhance the Drilling & Tripping Performance on Automated Rigs with Fully Automated Performance Measurement. Society of Petroleum Engineers. doi:10.2118/176786-MS.
Maidla, E. E., & Maidla, W. R. (2010, January 1). Rigorous Drilling Nonproductive-Time Determination and Elimination of Invisible Lost Time: Theory and Case Histories. Society of Petroleum Engineers. doi:10.2118/138804-MS.
Mandava, C., Husband, J., and Lockridge, M. 2017. Deconstructing Invisible Lost Time: KPIs, Analytics, Standardization Improve Well deliveries in Bakken. International Association of Drilling Contractors. https://www.drillingcontractor.org/deconstructing-invisible-lost-time-kpis-analytics-standardization-improve-well-deliveries-in-bakken-44170.
Mazerov, K. 2013. Automated Rig Activity Analysis Offers More Precise Method for Reducing NPT, Invisible Lost Time. International Association of Drilling Contractors. https://www.drillingcontractor.org/automated-rig-activity-analysis-offers-more-precise-method-for-reducing-npt-invisible-lost-time-27033.
Ouahrani, L., Haris, A. N. A., Suluru, S., Chiha, A., & Al Fakih, A. (2018, August 16). Invisible Lost Time Measurement and Reduction Contributes to Optimizing Total Well Time by Improving ROP and Reducing Flat Time. Society of Petroleum Engineers. doi:10.2118/192319-MS.